Unprecedented Low Levels of Genetic Variation and Inbreeding Depression in an Island Population of the Black-Footed Rock-Wallaby

Abstract: It has been argued that demographic and environmental factors will cause small, isolated populations to become extinct before genetic factors have a significant negative impact. Islands provide an ideal opportunity to test this hypothesis because they often support small, isolated populations that are highly vulnerable to extinction. To assess the potential negative impact of isolation and small population size, we compared levels of genetic variation and fitness in island and mainland populations of the black-footed rock-wallaby ( Petrogale lateralis [Marsupialia: Macropodidae]). Our results indicate that the Barrow Island population of P. lateralis has unprecedented low levels of genetic variation (  H _e = 0.053, from 10 microsatellite loci) and suffers from inbreeding depression (reduced female fecundity, skewed sex ratio, increased levels of fluctuating asymmetry). Despite a long period of isolation ( ∼ 1600 generations) and small effective population size (  N _e ∼ 15), demographic and environmental factors have not yet driven this population to extinction. Nevertheless, it has been affected significantly by genetic factors. It has lost most of its genetic variation and become highly inbred (  F _e = 0.91), and it exhibits reduced fitness. Because several other island populations of P. lateralis also exhibit exceptionally low levels of genetic variation, this phenomenon may be widespread. Inbreeding in these populations is at a level associated with high rates of extinction in populations of domestic and laboratory species. Genetic factors cannot then be excluded as contributing to the extinction proneness of small, isolated populations.     

Real-time PCR assay for detection and relative quantification of <Emphasis Type="Italic">Liocarcinus </Emphasis><Emphasis Type="Italic">depurator</Emphasis> larvae from plankton samples

Accurate species identification of decapod crustacean larvae is required to understand their population distributions, life cycle dynamics and interactions with their habitats. Analysis of plankton samples using morphological taxonomic methods and microscopy is time-consuming, requires highly skilled and trained operatives and may often be inaccurate. As complementary tools to classical identification methods, recent work has focused on the development of molecular approaches and shows their feasibility for species-specific identification. This study has developed real-time PCR assays utilising species-specific Taqman^® probes designed in the cytochrome oxidase I (COI) gene of Liocarcinus depurator, Necora puber, Carcinus maenas and Cancer pagurus. Our study then employed the probe and primers designed for L. depurator to obtain accurate identification and relative abundance estimates of L. depurator larvae in plankton samples collected between March 2005 and October 2006. Ranges of larval abundances were derived from a standard curve created from plankton samples spiked with a known number of larvae reared in the laboratory. Inhibition of the PCR reaction was shown to be an important factor and our results suggested that 0.1 ng of DNA as template provided accurate identification and avoided inhibition. Real-time PCR was shown to provide accurate species identification on unsorted plankton samples and could be suitable for the estimation of larval abundances in the plankton, although more work must be done to improve the accuracy of those estimations.     

Suitable environmental areas for commercial timber in the state of Rio de Janeiro,Southeastern Brazil

Environment, Development and Sustainability - In state of Rio de Janeiro, the Southeast Region of Brazil, 97.91% of the reforested areas contain exclusively species of Pinus and Eucalyptus. Due to...     

Forecasting air quality time series using deep learning

This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O₃) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O₃ exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours.

Implications: Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution concentration while only monitoring key parameters and without transforming the data set in its entirety, thus allowing real time inputs and continuous prediction.     

Approximate bias of the estimated proportion in group testing

The maximum likelihood estimator for estimating proportions by group testing is biased. An expression for the approximate bias has been previously presented, which enables the creation of a less biased estimator by removing the term of \(O(n^{-1})\). However, in this previous work the term of \(O(n^{-2})\) was incorrectly derived. This note gives a correct derivation, and examines the relative contribution of the two terms.     

Evaluating Ecological Condition Using Soil Biogeochemical Parameters and Near Infrared Reflectance Spectra

Rapid, repeatable assessment of ecological condition is critical for quantitative ecosystem monitoring. Soils provide a sensitive, integrative indicator for which sampling and analysis techniques are well defined. We evaluated soil properties as indicators of ecological condition (subjectively classified into minimally/moderately/severely degraded based on vegetative, hydrologic and edaphic cues) at 526 sites within Ft. Benning military installation (Georgia, USA). For each sample, we measured 17 biogeochemical parameters, and collected high-resolution diffuse reflectance spectra using visible/near infrared reflectance spectroscopy (VNIRS). VNIR spectra have been related to numerous soil attributes — we examine them here for diagnosing integrated response (i.e., ecological condition). We used ordinal logistic regression (OLR) and classification trees (CT) to discriminate between condition categories using both sets of predictors (biogeochemistry and spectra). Sixteen biogeochemical parameters were significantly different across condition categories; however, multivariate models greatly improved discrimination ([calibration, validation] accuracy of [69%, 66%] and [96%, 73%] for OLT and CT models, respectively). Important predictors included total C, total P, and Mehlich K/Ca/Mg. VNIR spectra further improved discrimination ([calibration, validation] accuracy of [74%, 70%] and [96%, 75%] for OLR and CT models, respectively). While spectra were comparably effective at discriminating minimally degraded sites, they were significantly more effective at discriminating severely degraded sites. Error rates across confounding factors suggest that watershed of origin and landscape position were the only important confounders, likely due to imbalanced sampling. We conclude that multivariate diagnosis improves accuracy, and that VNIR spectroscopy, which yields substantial cost and logistical improvements over conventional analyses, provides an effective tool for rapid condition diagnosis.     

Landscape resistance to dispersal: simulating long-term effects of human disturbance on a small and isolated wolf population in southwestern Manitoba, Canada

Landscape fragmentation affects wildlife population viability, in part, through the effects it has on individual dispersal. In addition, some forms of human disturbance impinge on dispersal without physically fragmenting habitats. Here, we use the term "landscape resistance" to capture constraints to dispersal that cannot be linked directly to fragmentation. The extent to which landscape resistance can influence population persistence is not well understood. Agricultural development over the past 60?years has resulted in considerable habitat fragmentation in the Riding Mountain National Park (RMNP) region in southwestern Manitoba, Canada. We examined how park boundaries, roads outside park boundaries and negative human attitudes have altered dispersal success and population persistence. We examined whether stochastic disturbance, representing infectious disease epidemics, further reduced long-term population persistence for various scenarios. Finally, we assessed whether the simultaneous occurrence of the three features had additive effects. We simulated dispersal using HexSim, a spatially explicit individual-based population model, parameterised with data on wolves (Canis lupus) in the RMNP region. Simulations that separately accounted for negative human attitudes and roads outside the park boundaries exhibited lower mean population size than those that ignored these details. Increasing deflection from park boundaries did not appear to have significant impacts. Our results did not indicate the presence of additive effects, and scenarios incorporating all three features had similar results as that of roads. Stochastic disturbance further reduced mean population size. Our results do illustrate how less-visible human disturbances (i.e. those that do not clearly alter landscape characteristics) can significantly limit dispersal and population persistence.     

A comparative assessment of molecular biological and direct microscopic techniques for assessing aquatic systems

Molecular techniques have clear value for community characterization; however, almost all previous datasets are based upon non-molecular measurements and it is hard to compare "old" data with "new" data because few correlations have been made. Therefore, the purpose of this evaluation was to simultaneously use molecular and non-molecular methods within the same sampling program to determine how data compare. Three methods were used for characterizing microbial populations in Lake 260 (L260) at Experimental Lakes Area (Ontario, Canada) during a whole-lake exposure study. Methods included whole-cell microscopic counts (for bacteria, cyanobacteria, algae and zooplankton), chlorophyll a, and small sub-unit (ssu)-rRNA hybridization using EUB-338, EUB-785, CYAN-785-a/b, EUCA-1379 and UNIV-1390 gene probes. Strong correlations were found between the EUB-785 probe signal and "bacteria minus cyanobacteria" direct counts, and the EUB-338 probe signal and "bacteria plus cyanobacteria" counts. Furthermore, the difference in probe signal between EUB-338 and EUB-785 (a presumptive signature for cyanobacteria and plastids) correlated with cyanobacterial direct counts and also with chlorophyll a. However, EUCA-1379 probe signal did not correlate with algae counts, and UNIV-1390 probe signal only correlated with total bacteria counts. Results suggest that, although ssu-rRNA methods are fast, reproducible, and specifically detect "viable" organisms, their use may be limited to non-eukaryotic populations unless new probes are developed that are more specific.     

Regional data refine local predictions: modeling the distribution of plant species abundance on a portion of the central plains

Species distribution models are frequently used to predict species occurrences in novel conditions, yet few studies have examined the consequences of extrapolating locally collected data to regional landscapes. Similarly, the process of using regional data to inform local prediction for species distribution models has not been adequately evaluated. Using boosted regression trees, we examined errors associated with extrapolating models developed with locally collected abundance data to regional-scale spatial extents and associated with using regional data for predictions at a local extent for a native and non-native plant species across the northeastern central plains of Colorado. Our objectives were to compare model results and accuracy between those developed locally and extrapolated regionally, those developed regionally and extrapolated locally, and to evaluate extending species distribution modeling from predicting the probability of presence to predicting abundance. We developed models to predict the spatial distribution of plant species abundance using topographic, remotely sensed, land cover and soil taxonomic predictor variables. We compared model predicted mean and range abundance values to observed values between local and regional. We also evaluated model prediction performance based on Pearson's correlation coefficient. We show that: (1) extrapolating local models to regional extents may restrict predictions, (2) regional data can help refine and improve local predictions, and (3) boosted regression trees can be useful to model and predict plant species abundance. Regional sampling designed in concert with large sampling frameworks such as the National Ecological Observatory Network may improve our ability to monitor changes in local species abundance.